Government Standard (GOST) is a 64-bit block cipher algorithm with 32 round, use a 256-bit key. The weakness of this algorithm is the keys so simple, than make cryptanalyst easy to break this algorithm. Least Significant Bit (LSB)  use to insert message into another form without changing the form of the cover after insertion. This research does by hiding encrypted ciphertext to image and hiding image into audio. This research use grayscale and RBG image with BMP and PNG format. Audio using music with wav format. Security analysis using differential analysis NPCR and UACI. Security analysis aims to calculate percentage from cover after hiding the message. The smaller the NPCR and UACI values, the higher the level of security the message is hidden. The results of the analysis of concealment in the image obtained by the average values of NPCR and UACI were 99.98% and 3.46% respectively. While the results of the analysis of hiding in audio obtained the average value of NPCR and UACI were 83.78% and 12.66% respectively.

Abstrak-Pertumbuhan dan perkembangan anak meliputi banyak aspek mulai dari perkembangan fisik, perkembangan mental, hingga memiliki kemampuan baru di setiap tahapan usia. Masing-masing anak dapat memiliki laju perkembangan yang berbeda-beda. Namun, ada beberapa hal yang perlu diperhatikan karena bisa jadi menunjukkan adanya gangguan perkembangan, termasuk gejala autisme. Apabila anak autisme tidak mendapat penanganan secara dini, kondisi autis akan menjadi permanen. Oleh karena itu tatalaksana terapi harus dilakukan pada usia sedini mungkin, yaitu dibawah usia 3 tahun. Dalam mendeteksi anak yang menderita autis memang tidak mudah dikarenakan butuh waktu hingga usia dua tahun untuk dapat memastikan anak benar benar mengidap autis. Autis sendiri tidak hanya dapat dideteksi oleh penalaran awam orang tua dari informasi yang didapatnya. Untuk membantu ditahap awal deteksi gangguan autisme pada anak, diperlukannya sebuah metode dalam mendiagnosis gejala yang ditunjukkan anak yaitu fuzzy expert system yang menggunakan metode inferensi fuzzy mamdani. Kata kunci-diagnosis dini, autis, inferensi fuzzy mamdani I. PENDAHULUAN Pertumbuhan dan perkembangan anak meliputi banyak aspek mulai dari perkembangan fisik, perkembangan mental, hingga memiliki kemampuan baru di setiap tahapan usia. Kemampuan baru pertahapan usia ini umum disebut sebagai development milestones (misalnya tersenyum, mengucapkan kata pertama, mulai berjalan, atau duduk sendiri tanpa ditopang) serta dapat digunakan oleh orang tua dan dokter untuk mengamati perilaku, kemampuan, dan perkembangan anak[1]. Masing-masing anak dapat memiliki laju perkembangan yang berbeda-beda. Namun, ada beberapa hal yang perlu diperhatikan karena bisa jadi menunjukkan adanya gangguan perkembangan, termasuk gejala autisme. Gejala autisme sangat bervariasi. Sebagian anak berperilaku hiperaktif dan agresif atau menyakiti diri, tapi ada pula yang pasif. Mereka cenderung sangat sulit mengendalikan emosinya dan sering tempertantrum (menangis dan mengamuk). Kadang-kadang mereka menangis, tertawa atau marah-marah tanpa sebab yang jelas. Dalam mendeteksi anak yang menderita autis memang tidak mudah dikarenakan butuh waktu hingga usia dua tahun untuk dapat memastikan anak benar benar mengidap autis. Autis sendiri tidak hanya dapat dideteksi oleh penalaran awam orang tua dari informasi yang didapatnya. Oleh karena itu, untuk membantu ditahap awal deteksi gangguan autisme pada anak, dirancanglah perangkat lunak berbasis android sebagai pakar (baik dokter ataupun psikiater) dalam mendiagnosa gejala yang ditunjukkan anak dengan menggunakan metode inferensi fuzzy mamdani. Logika fuzzy sendiri sering digunakan dalam pemecahan masalah yang menjelaskan sistem bukan melalui angka-angka, melainkan secara linguistik, atau variabel-variabel yang mengandung ketakpastian/ ketidaktegasan. Nilai-nilai yang bersifat tidak pasti ini berdasarkan penalaran yang mengkombinasikan variabel numerik, variabel linguistik, dan aturan-aturan. Adapun perangkat lunak yang digunakan dalam diagnosis ini tersebut berupa ...

Cryptography is knowledge of encoding data to ensure the confidentiality, security, validity and integrity of data. Cryptography is divided into two namely classical cryptography and modern cryptography. One example of modern cryptography is the Electronic Code Book (ECB). Electronic Code Book (ECB) is a modern cryptographic method used to encrypt and decrypt text, images and more. The image is formed from several pixels which consist of several bits in a pixel. Bits are divided into two namely Least Significant Bit (LSB) and Most Significant Bit (MSB).LSB is the four rightmost bits while MSB is the leftmost four bits of a pixel. The purpose of this study is to compare the level of security of Electronic Code Book (ECB) image security results with the results of securing an Electronic Code Book (ECB) modified image. The data used in this study are 8 RGB and Greyscale images also a key in the form of one ASCII character. The results obtained show that securing images with modified Electronic Code Book (ECB) is safer than securing images with Electronic Code Book (ECB) based on histogram analysis, differential analysis and correlation coefficients. Keywords: ASCII, Electronic Code Book (ECB), Most Significant Bit (MSB)

Non-linear equations system is a collection of some non-linear equations that will find the best solution. Finding the solution of non-linear equations system usually by using analytical method, but there are some complex cases that cannot be solved analytically, so new methods are needed to solve them. One method that can be used to solve non-linear equations system is by using metaheuristic algorithm. This research aims to solve non-linear equations system with complex roots by using metaheuristic algorithm. The metaheuristic algorithm used in this research is Particle Swarm Optimization (PSO), Firefly Algorithm (FA) and Cuckoo Search (CS). The input of this research is non-linear equations system that will be tested and parameters of the PSO, FA, and CS algorithm. Non-linear equations system which is the object of problem is polynomial function and transcendent function, which include logarithmic function, first degree trigonometric function and exponential function. The resulting output is an approximation of the complex roots and function value. Then the obtained solution of non-linear equations system compared with the result of accuracy by finding the value of the function f(x) which is closer to zero. The result of this research obtained by comparing the value of the function produced by each algorithm showed that Particle Swarm Optimization (PSO) algorithm is better at solving non-linear equations system with complex roots because the value of the resulting function is close to zero.

Optimization problems have become interesting problems to discuss, including the knapsack problem. There are many types and variations of knapsack problems. In this paper, the authors introduce a new hybrid metaheuristic algorithm to solve the modified bounded knapsack problem with multiple constraints we call it modified bounded knapsack problem with multiple constraints (MBKP-MC). Authors combine two popular metaheuristic algorithms, Particle Swarm Optimization (PSO) and Cat Swarm Optimization (CSO). The algorithm is named Hybrid Cat-Particle Swarm Optimization (HCPSO). The results of the implementation of the algorithm are compared with PSO and CSO algorithms. Based on the experimental results, it is known that the HCPSO algorithm is suitable and can reach to goodquality solution within a reasonable computation time. In addition, the new proposed algorithm performs better than the PSO and CSO on all MBKP-MC data used.